A Multi-scale Feature Extraction and Fusion Method for Diagnosing Bearing Faults

Abstract

Bearing fault diagnosis is vital to safeguard the heath of rotating machinery. It can help to avoid economic losses and safe accidents in time. Effective feature extraction is the premise of diagnosing bearing faults. However, effective features characterizing the health status of bearings are difficult to extract from the raw bearing vibration signals. Furthermore, inefficient feature extraction results in substantial time wastage, making it hard to apply in real time monitoring. A novel feature extraction method for diagnosing bearing faults using multi-scale improved envelope spectrum entropy (MIESE) is proposed in this work. First, bearing vibration signals are analyzed across multiple scales, and improved envelope spectrum entropy (IESE) is extracted from these signals at each scale to form an original feature set. Subsequently, joint approximate diagonalization eigen (JADE) is applied to fuse above feature set for effectively eliminating redundancy and generated a refined feature set. Finally, the newly generated feature set is input into support vector machines (SVM) to effectively diagnose bearing health status. Two cases studies are employed to demonstrate the reliability of the proposed method. The results illustrate the proposed method can improve the stability of extracted features and increase the computational efficiency.

Conflict of Interest Statement

The authors declare no conflicts of interest.